0

Dynamic correlation analysis of financial contagion:

Evidence from the Central and Eastern European markets

by

Manolis N. Syllignakis1

and Georgios P. Kouretas1*

October 2009

Abstract

This paper applies the Dynamic Conditional Correlation (DCC) multivariate GARCH

model of Engle (2002), in order to examine the time-varying conditional correlations,

to the index returns of seven emerging stock markets of Central and Eastern Europe.

We use weekly data for the period 1997-2009 in order to capture potential contagion

effects among the US, German and Russian stock markets and the CEE stock markets.

The main finding of the present analysis is that there is a statistically significant

increase in conditional correlations between the US and the German stock returns and

the CEE stock returns particularly during the 2007-2009 financial crisis, implying that

these emerging markets are exposed to external shocks with a substantial regime shift

in conditional correlation. Finally, we demonstrated that domestic and foreign

monetary variables, as well as exchange rate movements have a significant impact on

the corresponding conditional correlations.

Keywords: Central and Eastern European emerging stock markets, financial

contagion, dynamic conditional correlations, financial crises.

JEL classification: C22, G15

*An earlier version of this paper was presented at the International Conference MAF 2008 on

Mathematical and Statistical Methods for Actuarial Sciences and Finance, University Ca‟ Foscari of

Venice, 26-28 March 2008 as well as at the 6th

Summer School on Stochastic Finance, Athens 20-23

July 2009 and thanks are due to conference participants for many helpful comments and discussions.

This paper has also benefited from comments by seminar participants at the Free University of

Amsterdam and Athens University of Economics and Business. Syllignakis acknowledges generous

financial support from PENED 2003 under research grant #03ED46 co-financed by E.U.-European

Social Fund (75%) and the Greek Ministry of Development-GSRT (25%). Kouretas acknowledges

financial support by a Marie Curie Transfer of Knowledge Fellowship of the European

Community's Sixth Framework Programme under contract number MTKD-CT-014288, as well as by

the Research Committee of the University of Crete under research grant #2257. We thank Panayiotis

Diamandis, George Karathanassis, Dimitris Georgoutsos, Dimitris Moschos and Leonidas Zarangas for

many helpful comments and discussions. The usual caveat applies.

1Department of Business Administration, Athens University of Economics and Business, 76 Patission

Street, GR-10434, Athens, Greece.

*Corresponding author. Email: kouretas@aueb.gr

1

1. Introduction

It is well documented that stock return correlations vary over time. According

to Ang and Bekaert (1999) and Longin and Solnik (1995, 2001), correlations among

market returns tend to decline in bull markets and to rise in bear markets. Moreover,

the fact that international stock market correlation is significantly higher during the

periods of volatile markets (i.e. stock market crises periods) has become the accepted

perception (Lin et al., 1994). The global scale of the October 1987 stock market crash,

the Asian crisis in 1997 and the Russian default in 1998, have created a growing

concern among researchers and policy makers to investigate the various aspects of

international stock market relations, since the findings are significant both in

application of passive and active international investment strategies and the

identification of the channels of shock spreading from one market to the other. Low

international correlation across markets is the starting place of global portfolio

diversification strategy (Lessard, 1973 and Solnik, 1974). If correlations between

stock returns are high, a loss in one stock is likely to be accompanied by a loss in

other stocks as well. Therefore, diversification benefits are greater, when the

correlation between the stock returns is low. On the other side, the identification of

significantly increased correlation of stock market returns can be regarded as an

evidence of existence of the contagion effect.1

The main body of the current literature explores the links among the

developed stock markets (Hamao et al., 1990; Theodossiou and Lee, 1993; Longin

and Solnik, 1995; Meric and Meric, 1997; Goetzmann et al. 2001; Cappiello et al.,

2006; Kim et al., 2005), while some recent studies have extended this line of research

1 Forbes and Rigobon (2002) define contagion as significant increases in cross market co-movement, while any

continued high degree of market correlation suggests strong linkages between the two economies and is considered

to be interdependence. Therefore, the existence of contagion must involve a dynamic increase in correlation in the

aftermath of a crisis event.

2

to the linkages between the emerging and developed stock markets (Bekaert and

Harvey, 2000; Chen et al., 2002; Yang, 2005; Chiang et al., 2007; Phylaktis and

Ravazzolo, 2005). However, even though most of the aforementioned studies have

focused on emerging markets in Asia and Latin America, evidence on stock market

linkages of the emerging markets in Europe, remains relatively limited.

Gelos and Sahay (2000) investigate the impact of various external crises on

Central and Eastern European (CEE) stock markets. They found increased financial

market correlation since 1993, particularly around the time of the 1998 Russian crisis.

The Hungarian market appeared to be highly affected by that crisis. This finding is

consistent with Schotman and Zalewska (2006) who documented that the Hungarian

market was the most and the Czech market was the least sensitive to the 1997

Southeast Asian and 1998 Russian crises, a finding that may be explained by the fact

that among the three emerging markets discussed in that study the Hungarian market

had the highest foreign share ownership level and the Czech market the lowest.

Moreover, Wang and Moore (2008) documented a higher level of the stock market

correlation between three emerging CEE markets and the aggregate eurozone market

during the period after the Asian and Russian crises and also during the post-entry

period to the European-union. Furthermore, Gilmore and McManus (2002) examined

the short and long-term relationships between the US stock market and three CEE

emerging markets (Hungary, Poland, Czech Republic) over the 1995-2001 period and

he found that low short-term correlations between the CEE markets and the US

existed, whereas the application of the Johansen cointegration procedure indicated

that there is no long-term relationship among them. Additionally, Scheicher (2001)

found evidence of limited interaction between some of the CEE markets and the

major markets for daily stock market volatility.

3

Voronkova (2004) showed the existence of long-run links between the UK, the

German, the French and three Central European stock markets (Hungary, Poland,

Czech Republic) using daily data for the period 1993-2002, conditionally that

structural changes are properly accounted for. In a similar vein, Syriopoulos (2004,

2007) documented the existence of a long-run relationship between the US, the

German and four CEE stock markets (Hungary, Poland, Czech Republic and

Slovakia), using Johansen‟s cointegration methodology over the period between 1997

and 2003, while he insists that CEE markets tend to display stronger linkages with

their mature counterparts rather than their neighbors. Finally, Syllignakis and

Kouretas (2010) provided evidence that the stock markets of the Central and Eastern

European countries are partially integrated with the mature US and German stock

markets, since they share a significant common permanent component, which drives

the system of these stock exchanges in the long-run, with the Estonian market

appeared to be segmented. Furthermore, they also argued that the 2007-2009 global

financial turmoil had a negative effect on the convergence process.

The issue of contagion among stock markets has come to the surface once

again as a result of the financial crisis of 2007-2009. The CEE countries have been hit

dramatically by the events that originated in the mortgage subprime US market that

was eventually turned to a credit and financial crisis. Thus, as a result of the 2007-

2009 financial crisis investors in the over borrowed speculative hedge funds, private

equity and other institutional investors have withdrawn almost all their investments

from the emerging markets and certainly from the CEE stock markets. Facing

bankruptcy, these institutional investors moved to liquidate most of their stocks,

bonds and currencies from the CEE and other emerging markets and invested instead

in safer assets like the US government bonds. As a result the stock markets of the

4

CEE countries lost over 50% of their value between June 2008 and November 2008

while their currencies have been devalued substantially.

Hungary was the country which has been hit hardest by the crisis and faced

severe economic and financial problems. It had a huge current account deficit and was

forced to raise its basic interest rate from 8.5% to 11.5% in an effort to prevent the

depreciation of the Hungarian fiorin. However, this intervention policy did not work

and its currency continued to depreciate against the euro and the dollar. This fall in

value of the domestic currency resulted in a substantial increase in the value of its

external debt, forcing Hungary to ask for a 16.5 billion dollar loan from the IMF and

another 5 billion euro loan from ECB in an attempt to ease the severe consequences

for its economy. Almost all other CEE economies faced significant problems. Estonia

also faced an economic recession whereas the Romanian currency depreciated from

May 2008 to November 2008 as a result of the substantial increase in its budget

deficit, its current account deficit and its external debt which led to the reduction of its

credit ratings by Standard and Poor‟s and Fitch. Even the currencies of Poland and the

Czech Republic, which in the previous years had been quite stable, went under

substantial pressure due to the capital flight which led to a reduction in their values

against the euro.

Eichengreen and Steiner (2008) argued that part of the problem during this and

past financial crises is the incurrence of liabilities that are denominated in foreign

currency. Although some emerging economies have learned after the crises of 1994-

2002 the negative effects that currency mismatches can create, several CEE countries

borrowed in foreign currency during the subsequent cycle. This mistake was repeated

by Hungary and to a lesser extent by Poland. Eichengreen and Steiner (2008)

interpreted these transactions as a failed “convergence play” among CEE countries

5

considered to be on the path to joining the euro resulted in this way to another episode

of the carry trade observed in foreign exchange and money markets during the last

five years.

In this paper we provide further analysis to the issue of contagion by

examining the correlations among seven stock markets of the CEE economies which

have been recently become members of the European Union. We coupled our analysis

by linking the volatility of returns of these stocks markets with those of the US,

German and Russian stock markets. We chose the US and German stock markets

since they have an influential role in emerging European stock markets due to their

significant investment flows in these markets. In addition, Russian has been

historically an important trade partner of the CEE economies and trade flows is

considered to be an important channel for the spread of currency crises particularly in

the case of geographic proximity. We conducted our analysis with the application of

the Dynamic Conditional Correlation (DCC) multivariate GARCH models developed

by Engle (2002). We then looked into the impact that the 1997-1998 Asian and

Russian financial crises, the 2000-2002 dot-com bubble and the 2007-2009 financial

crisis had on the stock markets of the CEE economies. Finally, we investigated

potential explanatory factors that may drive the stock market conditional correlations.

Several important findings stem from our analysis. First, the examination of the

estimated correlation coefficients between the stock returns of the US and German

and the corresponding returns of the CEE stock markets are statistically significant

providing evidence in favor of the influential role of these two mature markets on the

CEE emerging stock markets. By contrast the Russian stock market has limited

influence on the stock returns of the CEE markets. Second, based on the plots of

pairwise conditional correlation coefficients it is revealed that these were increased in

6

magnitude substantially during the 1997-1998 Asian and Russian crisis, whereas

during the 2007-2009 financial crisis the conditional correlation coefficients rose

dramatically and they reached their highest value in December 2008. These results

may also suggest that during the periods of the crises and in particular in the second

half of 2008, the increase in equity market correlations could be attributed to herding

behaviour that is the observed contagion effects are investor induced through portfolio

rebalancing. Third, we provided evidence that these financial crises had a statistically

highly significant effect on the conditional correlations suggesting that these emerging

markets are exposed to external shocks with a substantial regime shift in conditional

correlation. Finally, we demonstrated that domestic and foreign monetary variables,

as well as exchange rate movements have a significant impact on the corresponding

conditional correlations.

The rest of the paper is organized as follows. In section 2 we discuss the

financial liberalization process and market characteristics of the CEE economies.

Section 3 presents the econometric methodology. In section 4 we discuss the data and

the empirical results and section 5 provides our conclusions.

2. Central and Eastern European emerging stock markets

2.1 Market characteristics

Following the change in political regime in the early 1990s a key element of

the transition process towards adopting the mechanisms of a market economy was the

re-establishment of the capital markets in the Central and Eastern European countries.

One of the main objectives of the reformers in the post-communist countries was the

creation of private ownership via privatization of state-owned enterprises. Moreover,

legal structures for ownership rights, corporations and contracts, order execution,

7

transparency in transaction of shares and the abolishment of restrictions in capital

flows have been established. The first stock exchange that reopened in the area was

the Ljubljana Stock Exchange (LJSE), on March 29, 1990, followed by the Budapest

Stock Exchange (BSE), on June 21, 1990, and the Warsaw Stock Exchange (WSE) on

April 16, 1991.

The accession of these countries to the European Union on May 1, 2004, gave

new perspectives on these markets and attracted the interest of many investors

worldwide, who previously refrained from investing in CEE markets because of real

or perceived political, liquidity and corporate governance risks.2 However, the first

fifteen years of operation of the emerging European stock markets have been

characterized by several events, such as the Asian crisis in 1997, the Russian default

in 1998 and most recently by the credit and financial crisis of 2007-2009, which led to

financial instability for the entire region and affect the confidence of investors that

have started investing in those markets.

In Table 1 we provide an overview of important characteristics of the

examined stock markets in Central and Eastern Europe, including the market

capitalization, the number of listed companies and the market (index) annual returns.

Therefore, it is shown that the larger stock markets in the CEE region, in terms of

market capitalization at the end of 2008, are those of Poland, the Czech Republic and

Hungary, with market capitalization of 90.81, 41.16 and 18.46 billion dollars

respectively.3 Moreover, the smaller market in the region is this of Estonia with

market capitalization of only 1.31 billion dollars. As a result of the different

2 During the period from January to December of 2004, the CEE stock exchanges recorded significantly high

returns. The Slovakian stock exchange recorded a return of 83.9%, the Hungarian 57.2%, the Estonian 57.1%, the

Czech 50.9%, the Polish 27.9% and the Slovenian recorded a return of 24.7%. 3 Compared to world-developed markets (NYSE, LSE, Euronext and Deutsche Börse) the CEE equity markets

remain thin in terms of market capitalization. However, the Warsaw stock exchange is bigger in terms of market

capitalization than several eurozone equity markets like the Wiener Börse, the Irish SE and the Athens SE.

8

approaches to privatization pursued by the CEE countries, the examined stock

markets had substantially different patterns of growth, in terms of the listed firms. For

instance, the number of firms listed on the Czech and Slovakian stock exchanges was

initially large, following the first of several mass waves of privatization. Since then,

the majority of those firms have been delisted, because of the lack of liquidity and the

overly stricter listing requirements.4 However, in other exchanges, like the Polish one,

the number of listed firms has grown slowly, as a result of a steady approach to the

implementation of the privatization scheme. The number of listed firms in the

Warsaw stock exchange at the end of 2008 was 458.

2.2 Financial liberalization

The international financial integration which increased dramatically in the last

three decades resulted to the gradual removal of controls on capital account

transactions and the deregulation of the domestic financial sector. This international

financial liberalization move was initiated in the developed countries after the

collapse of the Bretton Woods system. It was followed by a first wave of financial

liberalization by several Latin American and Southeast Asian countries in the late

1970s. Financial liberalization programs were further implemented in these two

regions in the late 1980s and early 1990s. During that period several Western

European countries also removed all capital controls as a prerequisite of the formation

of the European Monetary Union. The countries of CEE have been the most recent

group of economies which gradually adopted policies for the abolishment of capital

controls which was coupled with the re-opening of the stock exchanges in the region.

4 At most CEE exchanges only a minority of the companies are listed at the official and regulated markets, where

the listing requirements are much higher than other developed exchanges. On the contrary, there is large

concentration of listings in the free market (unregulated) segments, since these listings impose no costs on the

companies.

9

Although financial liberalization may have several positive effects on the

operation of financial, investment and growth there are also arguments against

complete financial liberalization. As Kaminsky (2008) argued the severity of the

current crisis has raised the question of whether modern liberalized financial markets

lead to more problems than they solve. Kaminsky and Schmukler (2008) further

demonstrated that the empirical evidence on the effects of financial liberalization is

rather mixed. One the one hand it is argued that the deregulation of financial markets

was the main cause of the current crisis as well as of all previous crises since the

1970s. On the other hand it is claimed that financial liberalization leads to more

efficient allocation of capital, increasing productivity and growth and helps the

financial markets to function much better. Although these two lines of thought seem

to be conflicting Kaminsky and Schmukler (2008) provide a framework that leads to a

reconciliation of these two arguments. Therefore, they argue that if we consider a

time-varying nature of the financial liberalization then the removal of capital controls

may trigger, in the short-run, financial booms and busts and subsequent output

collapses in those economies in which financial markets exhibit substantial

distortions. However, in the long-run, financial liberalization may lead to

improvements in institutions and accountability of investors eventually promoting

financial and economic stability.

This time-varying nature of financial liberalization may explain the capital

flow reversals which has been observed during the financial crisis of 2007-2009, since

foreign investors liquidate their portfolio investments in the CEE countries in order to

invest in the mature stock markets in a typical „flight to quality‟ movement. These

capital reversals followed the investment boom of the 1990s and early 2000s in the

capital markets of the CEE countries. Such capital flow reversals have also occurred

10

in the 1980s and in the late 1970s and as Kaminsky (2008) demonstrated sudden stops

as being an important source of financial crises and contagion among the international

financial markets.

In Table 2, information is provided regarding the date when the CEE capital

markets opened to foreign investors, following the methodology proposed by Bekaert

and Harvey (1995) and Bekaert (1995).5 According to that information, most

restrictions were lifted from the markets examined between 1996 and 1999. The

Czech market was the first that took certain measures towards official capital market

liberalization, while the Slovenian market was the last. However, it is important to

point out that the legal restrictions on foreign participation were lifted gradually. The

first country that issued an ADR was Hungary in 1992 and the last was Estonia in

1998. Moreover, the fifth and the sixth rows of Table 1 report the number of mutual

funds (UCITS) or ETFs that operates in each country. The investment funds or trusts

are considered as a mean for institutional investors to invest in the local markets.

Therefore, the most active funds the most open are the markets to foreign investors.

Specifically, Hungary is the country with the most active funds (129), while Romania

is the one with the least (6).

3. The DCC model

In this paper we apply the multivariate GARCH model proposed by Engle

(2002), to estimate dynamic conditional correlations (DCC) between the Central and

Eastern European stock market returns and those of the US, Germany and Russia

respectively. For a number of reasons the multivariate DCC-GARCH model is

5 Bekaert and Harvey (1995) proposed an indicator for characterizing the situation in which the emerging markets

were opened to foreign investors considering a multitude of elements including: the official date of the capital

market liberalization, the date of the ADR (American Depository Receipts) introduction on the market and the date

of the first country fund (FCF).

11

simpler in its estimation over the computationally intensive multivariate VEC model

and its variants described in Engle and Kroner (1995). First, the number of the

estimated parameters grows linearly with the number of stock returns and therefore

the model is relatively parsimonious. Secondly, another advantage of the DCC-

GARCH model is that requires a two-step estimation procedure, so that the number of

parameters to be estimated simultaneously is too small. In the first step univariate

GARCH models are estimated for each stock return, while in the second one the

standardized residuals obtained from the first step are used in order to estimate

correlation coefficients and thus accounts directly for heteroskedasticity. The resulting

estimates of time-varying correlation coefficients enable us to study the correlation

behavior between the national stock-index returns during a period with multiple

regime shifts in response to shocks and crises events. The stock market returns are

assumed as the following process:

1 1 2 1 ,

1, 2, , 1, 2, , 1

, (1)

( , ,....., ) , ( , ,........, ) and ~ (

dev

t t t i t

t t t n t t t t n t t t

r r r

r r r r I N

0, ).tH

where tr is a 1 x n vector of stock return index and t is a 1 x n vector of residuals

conditional on the information at time period 1t , 1tI with N multivariate normal

distribution. The first order autoregressive term is used to account for the

autocorrelation of stock returns. The lagged U.S., German and Russian stock return is

included in the mean equation, respectively, in order to account for a global or local

factor (Chiang et al., 2007). The inclusion of this factor is also based on the empirical

finding that U.S., German and Russian stock returns play an important role in

determining stock returns in CEE countries, while CEE stock returns have no

12

significant dynamic effect on them. The multivariate conditional variance is specified

as:

(2)t t t tH D R D

where tD is a ) x ( nn diagonal matrix of time-varying standard deviations obtained

from univariate GARCH(1,1) specifications with ,ii th on the i th diagonal,

ni ,........2,1 .6

tR is the ) x ( nn time-varying correlation matrix.7 The DCC

specification is given as:

, , 1 , 1 , 1

,

,

, ,

(1 )

(3)

, 1,2,..., , and .

ij t ij ij t i t j t

ij t

ij t

ii t jj t

q a b bq a

q

q q

i j n i j

where tijq , is the ji, element of iQ ) x ( nn time-varying covariance matrix of the

standardized residuals , , ,/i t i t ii th , ij is the unconditional correlations of , ,i t j t

and a , b are nonnegative scalar parameters satisfying 1a b . Finally, ,ij t is a

typical element of the time-varying correlation matrix )( tR .

As proposed by Engle (2002), the log-likelihood of the estimators can be

written as:

2 1 1 1

1

1( ) [( log(2 ) log ) (log )] (4)

2

T

t t t t t t t t t t t

t

L n D D D R R

6 The univariate GARCH(1,1) specification is specified as:

2 , for = 1,2,...., ., ,1 , 1 ,1 , 1h h i nii t i i i t i ii t

7 The correlation matrix Rt is obtained from the scale of the covariance matrix Q

tthat does not

generally have ones on the diagonal: 1/ 2 1/ 2 1/ 2

11, ,( ( )) ( ( )) , ( ( )) (1 / , ..., 1 / ).t nn tt t t tR diag Q Q diag Q diag Q diag q qt

13

where n is the number of equations, T is the number of observations and is the

vector of parameters to be estimated. The first part of the likelihood function is the

sum of the individual GARCH likelihoods and can be maximized in the first step over

the parameters in tD . In the second step the correlation coefficients can be estimated

given the parameters estimated in the first step from the maximization of the second

part of the likelihood function.

4. Data and preliminary empirical results

The data used in this paper are weekly stock-price indices from October 3,

1997, through February 13, 2009, for the equity markets of seven new European

Union member states.8 The data set consists of the local stock indices of Czech

Republic (PX), Estonia (TALSE), Hungary (BUX), Poland (WIG), Romania (BET),

Slovakia (SAX12) and Slovenia (SBI). Moreover, the S&P500 index is used to

represent the U.S. equity market, the DAX index the German market and the RTS

index the Russian market. The inclusion of the German and the U.S. markets in the

sample is due to the fact that both markets serve as a regional and global factor in the

region, respectively.9 Moreover, the Russian market is the biggest stock market in

Eastern Europe and has an influential role in emerging European stock market

movements, due to its significant trade flows with these markets. All national stock-

price indices are used in local currency terms and are based on weekly closing prices

8 The starting date of our sample reflects the earliest data available for Romanian stock index. Weekly data are

used due to the presence of more noise with higher frequencies, such as daily data. 9 We select Germany and the U.S. as the key developed markets because these markets are the biggest, in terms of

market capitalization, in North America and the Eurozone, respectively, and they can serve as proxies for the rest

mature markets in both regions, in depicting possible linkages with the emerging European stock markets

examined. Furthermore, the German and the U.S. markets have an influential role in emerging European stock

market movements due to their significant investment flows in these markets (see Table 1).

14

for each market.10,11

These stock market indices are transformed into weekly rates of

returns taking the first difference of the natural log of each stock-price index. The

source of the data is the Datastream International.

The summary statistics of stock-index returns in the seven Central and Eastern

European markets, the US, German and Russian markets are presented in Table 3.

Specifically, we report information on the mean, standard deviation, skewness

coefficient, kurtosis coefficient, the Jarque-Bera normality test, and the Ljung-Box

test (LB). As expected with emerging equity markets, the index returns series are

negatively skewed (with the exception of Slovakia) and leptokurtic. Moreover, the

Jarque-Bera test statistic reveals the typical non-normality of high frequency financial

time series. This finding suggests that for these markets, big shocks of either sign are

more likely to be present and that the stock returns series may not be normally

distributed. Furthermore, the results also reveal that the emerging markets of CEE

have higher Sharpe ratios than those reported for the developed equity markets.

Slovakia is the market with the highest Sharpe ratio (0.306) while Estonia is the one

with the lowest (-0.221). In addition most of the stock return series are found to

exhibit significant autocorrelation as it is suggested by the Ljung-Box test statistic.

Figure 1 provides plots of the weekly stock returns for each market and it is

shown that a clustering of larger returns volatility around and after 1997-1998 when

the Asian and Russian financial crisis took place. Moreover, a significantly higher

variation of the weekly stock returns was observed during the period of the 2007-2009

financial turmoil.

10 When data were unavailable, because of national holidays, bank holidays, or any other reasons, stock prices

were assumed to stay the same as those of the previous day. 11 Expressing the stock price indices in their national currencies restricts their changes to the movements in the

stock prices only, avoiding distortions induced by numerous devaluations of the exchange rates that have taken

place in the CEE region [see Voronkova (2004), Chiang et al. (2007), Syriopoulos (2007)].

15

The unconditional correlation between the stock returns for the markets under

examination is presented in Table 4 and it is shown that the unconditional correlation

of the emerging markets of Central and Eastern Europe with the mature markets of

Germany and the U.S. is relatively low and ranges between 0.063 and 0.539.

Furthermore, the unconditional correlation of the CEE markets with the Russian

market ranging from 0.08 to 0.53, with the Hungarian market to be the most and the

Slovakian market the least correlated with the Russian market.

4.2 The DCC model and estimation results

Table 5 (Panel A, B and C) presents the results of the multivariate DCC-

GARCH model that is estimated using the quasi-maximum likelihood method

described in Section 3. The constant term in the mean equation (eq. 1) is statistically

significant for all markets except for Slovakia. The AR(1) term in the mean equation,

1 , is statistically significant and negative for Czech Republic and Hungary, while it

is statistically significant and positive for Estonia and Slovenia. However, the AR(1)

coefficient is not statistically significant for Poland, Romania and Slovakia. The

effect ( 2 ) of the US and German stock returns on CEE stock returns is highly

significant and consistently of large magnitude, confirming the influential role of the

US and German stock markets on the CEE stock markets. By contrast, the Russian

stock returns do not have any significant effect on CEE stock returns, with the

exception of Slovakia. The last two rows of Panels (A, B and C) report the estimates

of the DCC(1,1) parameters ba and in eq. 3. Both parameters are statistically highly

significant, revealing a significant time-varying co-movement. Moreover, the

conditional correlations also exhibit high persistence, with the average sum of the two

coefficients to be over 0.90 during the sample period. The remaining rows are

16

parameter estimates of the mean and conditional variance equations for the stock

market returns examined. Furthermore, it is revealed that the coefficients for the

lagged conditional volatility and 2 terms in the variance equation (given in footnote

6) are highly significant, justifying the appropriateness of the GARCH(1,1)

specification. The results indicate that the volatility persistence measure )( ba is

close to one for all the markets examined. These results lead to the conclusion that the

volatility in the GARCH models displays high persistence.

An advantage of the multivariate DCC-GARCH model is based on the fact

that we can obtain all possible pair-wise correlation coefficients for the index returns

in the sample and study their behavior during periods of particular interest, such as

periods of financial turmoil. Therefore, we are able to examine for possible contagion

effects between the markets which have been affected by a specific crisis. According

to Boyer et al. (2006), contagion can either be investor induced through portfolio

rebalancing or fundamental based. The latter can be associated to what have been

described by Forbes and Rigobon (2002) as interdependence, while the former case is

described in behavioral finance literature as herding. The herding behavior can occur

because investors are following other investors, which has been characterized by

Hirshleifer and Teoh (2003) as “Convergence of behaviors”. The result of such a

herding behavior is that a group of investors trading in the same direction over a

period of time. Some recent empirical studies (see Corsetti et al., 2005; Boyer et al.,

2006 and Chiang et al., 2007) used the dynamic conditional correlations measure to

investigate possible herding behavior in emerging financial markets during crises

periods.

We now move to the discussion of the estimated conditional correlation

coefficients. We estimated a regression equation for the conditional return

17

correlations on a constant and a trend in order to examine whether the conditional

correlations changed over time. Table 6 reports the regression results and it was

revealed that the average conditional correlations between the stock returns of US,

Germany and Russia and those of Czech Republic, Estonia, Hungary, Poland,

Romania, Slovakia and Slovenia were of the same magnitude as the unconditional

correlations. Moreover, the volatility of the conditional correlations ranged from

2.33% to 6.81%. These findings show that a statistically significant rise over time in

conditional correlations was detected for all the pairs examined (except for the pair

Czech Republic – Russia) at the 5% level of significance. This rise in correlations is

measured by the term, , which is equal to the difference between the last and first

fitted values. The increase in correlation is particularly evident for Romania ( =

128.4%) and Estonia ( = 58.97%), suggesting that these markets and the S&P 500

index have become more interrelated over the period analyzed. However, these

markets together with the Slovakian market are still the least correlated with the

developed markets (Germany and US) ranging on average from 0.06 to 0.29. These

results further suggest that the diversification benefits from a portfolio which includes

equities from mature markets alongside with equities from the CEE markets may have

decreased during the last decade due to the EU accession process of the CEE states

and the increased participation of foreign investors in the local markets. Therefore,

based on these findings we may argue that the stock markets of the CEE economies

are mainly influenced by the markets of the US and Germany due to the inclusion of

stocks from these emerging markets in international portfolios and the accession of

these economies in the European Union. These results also revealed that the

coefficients with respect to Russia are not statistically significant and this maybe an

18

indication against the argument that geographic proximity is a potential source of

contagion.

Figures 2-4 show pair-wise conditional correlation coefficients between the

stock returns of US, Germany and Russia and those of Czech Republic, Estonia,

Hungary, Poland, Romania, Slovakia and Slovenia during the period 1997-2009.

These time series patterns show that the pair-wise conditional correlations

incremented dramatically during the current financial crisis and reached their highest

level on December, 2008. Particularly, the conditional correlation between the S&P

500 index and the CEE markets increased by 120% on average during the period

September-December 2008. This dramatic increase in equity market correlations in

the second half of 2008 can be attributed to what have been previously described as

“Herding behavior”. According to this notion, in times of severe stress like that

experienced in 2008, disparate markets will all tumble together as investors scramble

to sell whatever they can and move into cash. The outcome of that behavior was more

intense during the current crisis since the advent of exchange-traded funds, or ETFs,

allowed investors to buy and sell equity portfolios in Central and Eastern Europe (e.g.

Lyxor ETF Eastern Europe, iShares DJ STOXX EU Enlarged 15, iShares MSCI

Eastern Europe 10/40) with the click of a mouse.

The correlation coefficients between the stock returns of US, Germany and

Russia and those of Czech Republic, Estonia, Hungary, Poland, Romania, Slovakia

and Slovenia increased but to a lesser extent throughout the 1997-1998 crisis period.

In general the pattern of the conditional correlation coefficients is quite similar around

the 1997-1998 crises period, where the correlation temporary peaks, particularly

among the developed markets of Germany and US and those of Czech Republic,

Hungary and Poland. It is interesting to note that during the 1997-1998 period despite

19

the significant effect of the two crisis events on the CEE markets, the conditional

correlations reached their highest level on October 1998, after the surprise cut of the

interest rate by the FED.12

The sudden increase in correlations during this period can

be associated with what was named by Alan Greenspan as flight to quality, the

phenomenon where investors substitute the risky stock assets with the most safe bond

assets. This spillover effect seems to be indicative of contagion effects across

emerging markets of Eastern Europe and underlines the significant role of the US

market in the region. Moreover, it is also fascinating to note that the pattern of

conditional correlation seems to coincide for Czech Republic, Hungary and Poland

throughout the rest of the period examined. It is quite evident also a common upward

trend since the entry to the EU in May 2004. In contrast, the pattern of conditional

correlation for the smallest CEE markets (Estonia, Romania and Slovakia) seems to

diverge from that of the rest CEE markets and follow mainly country-specific

movements.13

4.3 Statistical analysis of conditional correlation coefficients

We further study the time series behavior of conditional correlations and

provide additional insight on the impact that crises events and stock market volatility

have on their movements. Specifically, we estimate the following model:

3

, , , , ,

1

(5)ij t i t i j t j k k t ij t

k

h h DM

where ,ij t is the estimated pair-wise conditional correlation coefficients between the

stock returns of US, Germany, Russia and those of Czech Republic, Estonia,

12

In October 1998 there was a surprise cut of the interest rate by the FED to relieve the pressure in the

tight credit market, since lenders where very reluctant to provide new loans, after several crises in Asia,

Argentina and Russia. 13

The results for the dot-com bubble were found to be statistically insignificant.

20

Hungary, Poland, Romania, Slovakia and Slovenia, such that i = US, Germany,

Russia, j = Czech Republic, Estonia, Hungary, Poland, Romania, Slovakia and

Slovenia, th is the conditional volatility, ikDM , is the dummy variable which is used

to take into account the effects of three major financial crises: the crisis in Asia and

Russia (21/11/1997-30/10/1998), the dot-com bubble (10/03/2000-27/09/2002) and

the US Credit bust (Lehman collapse) (26/09/2008-End of sample period). A positive

i suggests that conditional correlations between the developed market indices and

the CEE market indices rise with the volatility of the developed market indices. On

the other hand, a negative i indicates that the correlations between the developed

market return series and the CEE market return series fall in periods of high volatility

in developed markets. This result implies that the usefulness of emerging European

stock markets as a diversification tool increases in periods of above average

developed market volatility.

In table 7, we report the estimates of the parameters of eq. (5). The results in

Panel A, regarding the i coefficients, reveal that the relation between conditional

correlation and S&P 500 volatility is not consistent throughout the cross section. The

evidence suggests that the slope coefficient i of equation (5) is positive for 4 CEE

markets and negative for 3 at the 5% level. Particularly, a 1% rise in market (S&P 500

index) risk leads to a 13.40% rise in correlation between the equity markets in US and

Hungary. In contrast, ceteris paribus, a 1% rise in market (S&P 500 index) risk leads

to a 25.69% fall in correlation between the equity markets in US and Czech Republic.

The different impact that market volatility has on conditional correlation confirms the

argument that the CEE markets behave in a different manner from one another and

cannot be treated as a unique market with the same characteristics. The estimated

21

slope coefficientsi in Panel B and Panel C are relatively lower comparing to those in

Panel A, revealing that the impact of the German and the Russian markets volatility

changes on conditional correlations is less significant.

We now turn to the discussion of the estimated dummy variable coefficients

which are reported in Table 7. The effect of the crisis events on the conditional

correlation coefficients between the developed market indices (S&P 500, DAX, RTS)

and the CEE market indices (BET, BUX, PX, SAX, SBI, TALSE, WIG) is of

particular interest; since in periods of market turbulence is higher the need and the

benefit that arise from the application of portfolio diversification techniques. The

results show that tDM ,1 is statistically significant and slightly negative, for the

majority of the pairs examined, indicating that the correlation during the period of the

1997 Asian and the 1998 Russian crises has to some extent declined.14

Regarding the

tDM ,2 coefficients the findings reveal that most of these coefficients are negative and

statistical significant at the 5%. This finding demonstrates that during the period

2000-2002 when the developed stock markets indices declined significantly the

correlation coefficients with the CEE markets were significantly lower than the post-

crisis period. Finally, during the crisis of 2007-2009, the correlation coefficients given

by the estimates of tDM ,3 , were positive and increased significantly in almost all the

cases. This finding is consistent with the co-movement paths shown in Figures 2-4

and supports the evidence of herding behavior during the recent stock market crash

that we mentioned above.

14

Based on the visual examination of increased correlations peaked on October 1998 we would expect

a positive coefficient for tDM ,1 . This apparent contraction may be due to the fact that the 1997-1998

period coincides with the beginning of the sample period which may affect the regression estimates.

However, data availability restricted us to have to have our sample started earlier for most CEE stock

markets.

22

4.4 Factor analysis of the conditional correlation coefficients

In the final stage of the present analysis we examined the time varying

conditional correlation coefficients between the stock returns of US, Germany and

Russia and those of Czech Republic, Estonia, Hungary, Poland, Romania, Slovakia

and Slovenia, in this section, we investigate the potential explanatory factors that

drive the stock market conditional correlations. Specifically, we estimate the

following linear equation for the CEE markets with respect to the US market15

:

, 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , / ,2 2 (6)ij t t cee t cee t cee t cee t us t us t us t cee us ij tIP IR M CR IP IR M ER

where IP, IR, M2 and ER stand for the monthly change of the seasonally adjusted

industrial production, the nominal one-month interbank rate, the money supply (M2),

and the exchange rate respectively. The CR factor is used in order to capture the effect

of sovereign credit rating changes reported by Standard and Poor‟s in CEE markets

during the period analyzed. The CR indicator is set equal to zero if there is no change

in the sovereign credit ratings, otherwise for an upgrade of one notch, we set CR=1;

for a downgrade of 2, we set CR= -2 whereas ,ij t is the end of month dynamic

conditional correlation between the CEE and the US stock market. We applied a

rolling regression methodology using a time window of 36 months, thus we have

5544 estimated coefficients in total.16

The rolling regression technique is applied in

order to identify a possible time variation of the regression coefficients. A summary

of the estimated coefficients is presented in Table 8. In each cell we report the number

of times the t -statistic of each explanatory variable is significant at 5% level of

significance. Overall we found empirical evidence of the time variation of the

15

We have also estimated equation (7) with respect to the German and Russian relevant macroeconomic

variables. The overall evidence is similar to that for the US case. To save space the results are

available upon request. 16

We estimated the model for 99 rolling time periods, 8 factors and 7 CEE markets.

23

explanatory power and the regression coefficients. The mean R2

statistic ranges

between 0.44 and 0.68. The coefficient on the interest rates of the CEE markets and

the US are significant at the 5% level for the majority of the rolling periods, implying

that the interest rates seem to affect the stock market return correlations. This finding

confirms our earlier argument that the October 1998 surprise interest rate cut by the

FED coincides with the temporal peak of the estimated conditional correlations. The

US money supply had also shown to be highly statistically significant with a positive

sign particularly during the last rolling periods in our sample. It implies that the

higher the US money supply, the higher the correlation between the US and the CEE

stock returns. Therefore, we may argue that to some extent changes in the US and

domestic monetary variables affect the corresponding conditional correlations. A

similar result is evident for the exchange rate coefficients. They are highly significant

with a positive sign particularly during the last rolling periods. This finding confirms

the empirical evidence that the CEE currencies have been greatly devaluated against

the US dollar during the period of the recent stock markets crash. The rest of the

estimated coefficients are not statistically significant for the majority of the rolling

periods in our sample.

5. Summary and concluding remarks

In this paper, we used the multivariate DCC-GARCH model to investigate the

relationship between the stock returns of various Central and Eastern European stock

markets during the 1997-1998 Asian and Russian financial crises, the 2000-2002 dot-

com bubble and the 2007-2009 credit and financial crisis. Our analysis was focused

on the examination of the pair-wise conditional correlation coefficients between the

stock returns of the U.S, Germany and Russia and those of the Czech Republic,

24

Estonia, Hungary, Poland, Romania, Slovakia and Slovenia by employing weekly

stock price data for the period October 1997 to February 2009. Following the recent

studies by Corsetti et al. (2005), Boyer et al. (2006) and Chiang et al. (2007), we

employed the Dynamic Conditional Correlations technique as the appropriate

framework to investigate the existence of increased correlation between the stock

returns of the mature stock market and those of the emerging markets as well as the

presence of contagion effects due to herding behavior during the periods of financial

turmoil.

The main findings that emerge from our analysis are summarized as follows.

First, based on the pair-wise correlation coefficients for the index returns resulted

from the estimated multivariate DCC-GARCH specification it was shown that the

conditional correlations increased significantly during the 1997-1998 crisis period

and reached their highest level on October, 1998, the week after the surprise cut of the

interest rate by the FED. Similarly, the pair-wise conditional correlation coefficients

increased enormously during the credit and financial turmoil of 2007-2009 and

reached their highest level in December 2008. This analysis of the pattern of the

conditional correlation coefficients during the period 1997-2009 provides substantial

evidence in favor of contagion effects due to herding behavior in the financial markets

of the Central and Eastern European emerging markets particularly around the 2007-

2009 financial turmoil.

Second, with the use of dummy variables we demonstrated that for the case of

the 2007-2009 financial turmoil the estimated dummy variable coefficients were

statistically significant and positive, confirming the evidence in favor of contagion

effects in the financial markets of the Central and Eastern Europe. In contrast, for the

cases of the Asian and Russian crises (1997-1998) and for the dot-com bubble the

25

contagion effect hypothesis is not consistent throughout the panel of the CEE

countries. These results further suggested that the episodes of financial turbulence and

in particular that of 2007-2009 had a statistically high significant effect on the

conditional correlations leading to the argument that these emerging markets are

exposed to external shocks with a substantial regime shift in conditional correlation.

Finally, the estimation of a stepwise linear regression of the conditional correlations

on a set of real and monetary variables showed that domestic and foreign monetary

variables as well as exchange rate movements have a significant impact on the

corresponding conditional correlations.

26

References

Ang, A. and G. Bekaert, 1999, International asset allocation with time-varying

correlations, NBER Working Paper, 7056.

Bekaert, G., 1995, Market integration and investment barriers in emerging equity

Markets, World Bank Economic Review, 9, 75-107.

Bekaert, G. and C. R. Harvey, 1995, Time-varying world market integration, Journal

of Finance, 50, 403-444.

Bekaert, G. and C.R. Harvey, 2005, Chronology of important financial, economic and

political events in emerging markets”, http://www.duke.edu/_charvey/chronology.htm

Boyer, B., T. Kumagai, and K. Yuan, 2006, How do crises spread? Evidence from

accessible and inaccessible stock indices, Journal of Finance, 61, 957-1003.

Cappiello, L., R. Engle and K. Sheppard, 2006, Asymmetric dynamics in the

correlations of global equity and bond returns, Journal of Financial Econometrics, 4,

537-572.

Chen, G.M., M. Firth and O.M. Rui, 2002, Stock market linkages: Evidence from

Latin America, Journal of Banking and Finance, 26, 1113-1141.

Chiang, T. C., B.N., Jeon, and H. Li, 2007, Dynamic correlation analysis of financial

contagion: Evidence from Asian markets, Journal of International Money and

Finance, 26, 1206-1228.

Corsetti, G., M. Pericoli and M. Sbracia, 2005, Some contagion, some

interdependence: more pitfalls in tests of financial contagion. Journal of International

Money and Finance 24, 1177-1199.

Dvorak, T. and R. Podpiera, 2006, European Union enlargement and equity markets

in accession countries, Emerging Markets Review, 7, 129-146.

Eichengreen, B. and K. Steiner, 2008, Is Poland at risk of a boom-and bust cycle in

the run-up to euro adoption?, NBER Working Paper No. 14438.

Engle, R.E., 2002, Dynamic conditional correlation: a simple class of multivariate

generalized autoregressive conditional heteroskedasticity models, Journal of Business

and Economic Statistics 20, 339-350.

Engle, R. and K. Kroner, 1995, Multivariate simultaneous generalized ARCH,

Econometric Theory, 11, 122–150.

Forbes K. and R. Rigobon, 2002, No contagion, only interdependence: Measuring

stock market comovements, Journal of Finance, 57, 2223-2262.

Gelos, G. and R. Sahay, 2000, Financial market spillovers in transition economies,

Economics of Transition, 9, 53-86.

27

Gilmore, C.G. and G.M. McManus, 2002, International portfolio diversification: US

and Central European equity markets, Emerging Markets Review, 3, 69-83.

Goetzmann W.N., L. Li and K.G. Rouwenhorst, 2001, Long-term global market

correlations, Journal of Business, 2005, 78, 1-38.

Hamao, Y., R. Masulis, and V. Ng, 1990, Correlations in price changes and volatility

across international stock markets, Review of Financial Studies, 3, 281-308.

Hirshleifer, D. and S.H. Teoh, 2003, Herd behaviour and cascading in capital markets:

a review and synthesis, European Financial Management, 9 , 25-66.

Kaminsky, G. L., 2008, Crises and sudden stops: Evidence from international bond

and syndicated-loan markets, NBER Working Paper No. 14249.

Kaminsky, G.L. and S.L. Schmukler, 2008, Short-run pain, Long-run gains: Financial

liberalization and stock market cycles, Review of Finance, 12, 253-292.

Kim, S.J., F. Moshirian, and E. Wu, 2005, Dynamic stock market integration driven

by the European Monetary Union: An empirical analysis, Journal of banking and

finance, 29, 2475-2502.

Lessard, D. R., 1973, International portfolio diversification: Multivariate analysis for

a group of Latin American Countries, Journal of Finance, 28, 619-633.

Lin, W.L., R.F. Engle and T. Ito, 1994, Do bulls and bears move across borders?

International transmission stock returns and volatility, Review of Financial Studies 7,

507-538.

Longin, F. and B. Solnik, 1995, Is the correlation in international equity returns

constant: 1960–1990?, Journal of International Money and Finance, 14, 3–26.

Longin, F. and B. Solnik, 2001, Extreme correlation in international equity markets,

Journal of Finance, 56, 649-676.

Meric, I. and G. Meric, 1997, Co-movements of European equity markets before and

after the 1987 crash, Multinational Finance Journal, 2, 137-52.

Phylaktis, K. and F. Ravazzolo, 2005, Stock market linkages in emerging markets:

implications for international portfolio diversification, Journal of International

Financial Markets, Institutions and Money, 15, 91-106.

Scheicher M. 2001, The comovements of stock markets in Hungary, Poland and the

Czech Republic, International Journal of Finance and Economics, 6, 27-39.

Schotman, P.C. and A. Zalewska, 2006, Non-synchronous trading and testing for

market integration in Central European emerging markets, Journal of Empirical

Finance, 13, 462–494.

28

Solnik, B., 1974, Why not diversify internationally rather than domestically?,

Financial Analysts Journal, 30, 48–54.

Syllignakis, M. and G.P. Kouretas, 2010, German, US and Central and Eastern

European stock market integration, Open Economies Review, forthcoming.

Syriopoulos, T., 2004, International portfolio diversification to Central European

stock markets, Applied Financial Economics, 14, 1253-68.

Syriopoulos, T., 2007, Dynamic linkages between emerging European and developed

stock markets: Has the EMU any impact?, International Review of Financial

Analysis, 16, 41–60.

Theodossiou, P. and U. Lee, 1993, Mean and volatility spillovers across major

national stock markets: Further empirical evidence, Journal of Financial Research, 16,

337-350.

Voronkova, S., 2004, Equity market integration in Central European emerging

markets: A cointegration analysis with shifting regimes, International Review of

Financial Analysis, 13, 633-647.

Yang, S.-Y., 2005, A DCC analysis of international stock market correlations: the role

of Japan on the Asian four tigers, Applied Financial Economics Letters, 12, 89-93.

Wang, P. and T. Moore, 2008, Stock market integration for the transition economies:

Time-varying conditional correlation approach, The Manchester School, 76, S116-

S133.

29

Table 1: Market Characteristics for CEE stock exchange markets

Number of listed firms

1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

Czech 1716 1670 320 304 195 151 102 79 65 55 39 21 21 29

Estonia --- 11 25 22 23 20 17 14 14 13 19 17 18 19

Hungary 42 45 48 54 66 59 58 49 50 47 44 41 41 43

Poland 65 83 143 198 221 225 230 216 203 230 241 265 375 458

Romania 9 17 76 126 122 109 60 60 57 55 59 53 54 64

Slovakia 850 970 918 833 830 866 888 510 452 389 306 187 160 193

Slovenia 17 45 78 90 130 149 151 135 134 140 116 100 87 84

Market Capitalization (billions of US dollar)

Czech 24,5 19,3 14,4 13,9 13,3 11,7 9,4 15,8 24,8 43,67 54,12 44.37 68.91 41.16

Estonia --- 0,694 1.11 0,519 1.18 1.81 1.73 2.43 3.79 6.20 3.53 3.42 2.81 1.31

Hungary 2.40 5.19 14,70 13,79 16,10 11,90 10,36 13,01 18,86 28,63 32,57 41.93 46.19 18.46

Poland 4.56 8.41 12,13 20,46 29,57 31,42 26,15 28,84 37,40 71,54 93,60 151.81 211.62 90.81

Romania 0,100 0,060 0,626 0,356 0,313 0,415 1.23 2.72 3.71 11,93 18,18 25.23 30.64 8.99

Slovakia 5.35 5.77 5.29 4.12 3.63 3.27 3.47 2.65 3.37 4.93 4.74 5.83 6.91 5.43

Slovenia 0,311 0,890 1.88 2.98 2.85 3.10 3.46 5.58 7.13 9.68 7.90 15.18 28.79 11.79

Stock Index Return (%)

96/95 97/96 98/97 99/98 00/99 01/00 02/01 03/02 04/03 05/04 06/05 07/06 08/07

Czech PX 26.7 -8,2 -29,4 24,2 -2,3 -13,6 17,0 41,6 50,9 46,9 9.7 14.1 -52.7

Estonia TALSE --- 61,90 54,86 39,3 19,1 4,7 46,8 34,4 57,1 50,7 28.9 -13.3 -62.98

Hungary BUX 176.4 93,5 -21,1 39,8 -11,0 -9,2 9,4 20,3 57,2 41,0 19.5 5.9 -53.3

Poland WIG 89.1 2,3 -12,8 41,3 5,0 -26,7 3,2 44,9 27,9 33,7 41.6 10.4 -48.2

Romania ΒΕΤ --- -24,2 -50,9 -2,6 9,1 -4,8 126,9 26,0 103,5 38,2 28.5 32.6 -70.3

Slovakia SAX 15.8 2,5 -49,5 -19,0 19,2 31,4 15,9 26,9 83,9 26.5 0.6 7.2 -19.4

Slovenia SBI -18.3 18,7 21,4 5,9 0,1 19,0 55,2 17,7 24,7 -5,6 37.9 78.1 -66.1

Sources: WFOE (World Federation Of Exchanges), FESE (Federation of European Securities

Exchanges), National stock exchanges.

30

Table 2: Indicators of financial liberalization for the CEE stock exchanges

Country Stock market

established Restrictions

lifted 1

st ADR N° of ETFs N° of UCITS

Czech June 1992 September 19941

June 1995 -- 32

Estonia May 1996 19965 December 1997 -- 46

Hungary July 1990 19962 July 1992 1 128

Poland January 1991 February 1997 February 1997 -- 47

Romania April 1995 March 19986 April 1998 -- 6

Slovakia January 1994 April 19984 April 1996 -- 43

Slovenia December 1989 19993 June 1997 3 8

Sources: Bekaert and Harvey, (2005), Dvorak και Podpiera (2006), WFOE (World Federation of

Exchanges), FESE (Federation of European Securities Exchanges) and National stock exchanges. ETFs

are the exchange traded funds and UCITS are the Undertakings for Collective Investment in

Transferable Securities.

1) More restrictions lifted in 1999

2) More restrictions lifted in 1998

3) Until 1999 foreign sales within 7 years taxed 12%. 25% foreign ownership limit.

4) More restrictions lifted in 2000

5) More liberalization in 2000. Restrictions on certain industries.

6) Some restrictions have been lifted since 1991, when the new FDI law (No. 35/1991) came into

effect, while more restrictions lifted on 2001.

31

Table 3: Descriptive statistics of weekly index return series

Notes: 3m and 4m are the coefficients of skewness and kurtosis of the weekly index returns respectively; JB is

the statistic for the null of normality; LB(12) denotes the Ljung-Box test statistic for serial correlation with 12 lags

respectively. (*) denotes statistical significance at the 5 percent critical level.

Czech

Republic Estonia Germany Hungary Poland Romania Russia Slovakia Slovenia U.S.

Ann. Return 2.67% -3.64% 0.69% 3.87% 2.61% 7.30% 2.10% 5.82% 8.43% -1.17%

Ann. Std. Dev.

24.69% 30.01% 26.10% 30.96% 25.79% 33.61% 49.83% 20.93% 17.72% 19.34%

Sharpe ratio (RF=3%)

-0.013 -0.221 -0.089 0.028 -0.015 0.128 -0.018 0.135 0.306 -0.216

3m -1.30 -1.18 -0.62 -1.26 -0.58 -0.66 -0.32 0.61 -1.69 -0.93

4m 14.95 11.31 7.78 12.52 5.92 9.76 7.22 7.53 14.55 10.02

J.B. 3704.21* 1847.20* 602.15* 2400.03* 244.28* 1173.91* 450.70* 545.28* 3587.05* 1304.18*

LB(12) 20.01* 47.30* 26.11* 22.35* 8.97 24.01* 35.28* 22.61* 92.86* 33.98*

32

Table 4: Correlation coefficients of weekly stock return series

Czech

Republic Estonia Germany Hungary Poland Romania Russia Slovakia Slovenia U.S.

Czech

Republic 1 0.300 0.523 0.635 0.594 0.385 0.460 0.096 0.367 0.480

Estonia 0.300 1 0.296 0.255 0.295 0.216 0.304 0.094 0.272 0.206

Germany 0.523 0.296 1 0.539 0.493 0.194 0.438 0.063 0.296 0.772

Hungary 0.635 0.255 0.539 1 0.626 0.252 0.536 0.140 0.358 0.474

Poland 0.594 0.295 0.493 0.626 1 0.270 0.444 0.087 0.307 0.480

Romania 0.385 0.216 0.194 0.252 0.270 1 0.215 0.055 0.357 0.193

Russia 0.460 0.304 0.438 0.536 0.444 0.215 1 0.080 0.216 0.366

Slovakia 0.096 0.094 0.063 0.140 0.087 0.055 0.080 1 0.077 0.076

Slovenia 0.367 0.272 0.296 0.358 0.307 0.357 0.216 0.077 1 0.277

U.S. 0.480 0.206 0.772 0.474 0.480 0.193 0.366 0.076 0.277 1

33

Table 5: Estimation results from the DCC-GARCH models Mean equations:

, ,

1 1 2 1 1, 2, 7, 1, 2, 7, 1 , where ( , ,....., ) , ( , ,........, ) and ~ (0, ).us ge rus

t t t t t t t t t t t t t t tr r r r r r r I N H

Variance equations: 2 , for = 1,2,....,7., ,1 , 1 ,1 , 1

h h iii t i i i t i ii t

DCC equation:

, , 1 , 1 , 1

,

,

, ,

(1 )

, where , 1,2,...,7, and .

ij t ij ij t i t j t

ij t

ij t

ii t jj t

q a b bq a

qi j i j

q q

Panel A. CEE - US

Panel B. CEE - GE

Czech

Republic Estonia Hungary Poland Romania Slovakia Slovenia U.S.

Panel A: Mean equations

Μ

0.003*

(2.722)

0.003*

(2.490)

0.003*

(2.546)

0.002*

(2.398)

0.004*

(2.596)

0.000

(0.175)

0.002*

(3.232)

0.001

(1.473)

-0.105*

(-3.425)

0.142*

(3.996)

-0.139*

(-3.957)

-0.054

(-1.651)

0.044

(1.200)

-0.016

(-0.396)

0.129*

(3.619)

-0.112*

(-2.963)

0.179*

(3.772)

0.149*

(3.345)

0.229*

(4.069)

0.184*

(4.038)

-0.007

(-0.114)

-0.001

(-0.012)

0.058*

(1.967) ---

Panel B: Variance equations

Ω

0.000*

(1.989)

0.000*

(3.761)

0.000*

(1.858)

0.000

(1.767)

0.000

(1.353)

0.000*

(2.526)

0.000*

(2.865)

0.000

(0.874)

A

0.097*

(3.693)

0.206*

( 5.350)

0.104*

(2.967)

0.091*

( 4.372)

0.074*

(2.531)

0.252*

( 4.368)

0.138*

( 4.107)

0.066*

( 4.486)

Β

0.869*

( 21.83)

0.761*

( 21.08)

0.872*

( 19.09)

0.895*

( 36.44)

0.916*

( 25.12)

0.749*

( 15.67)

0.828*

( 21.83)

0.937*

( 61.35)

Pers. 0.966 0.967 0.976 0.986 0.99 1.00 0.966 1.00

Panel C: Multivariate DCC equation

A 0.009* (2.571)

B 0.970* (47.169)

Czech

Republic Estonia Germany Hungary Poland Romania Slovakia Slovenia

Panel A: Mean equations

μ

0.003*

(3.032)

0.003*

(2.844)

0.003*

(3.276)

0.004*

(3.219)

0.003*

(2.703)

0.004*

(2.426)

0.000

(0.193)

0.003*

(3.152)

-0.094*

(-2.887)

0.146*

(3.453)

-0.050

(-1.373)

-0.176*

(-4.966)

-0.048

(-1.486)

0.029

(0.766)

-0.018

(-0.376)

0.119*

(3.072)

0.086*

(2.829)

0.077*

(2.695) ---

0.172*

(4.327)

0.097*

(3.255)

0.053

(1.171)

0.009

(0.280)

0.042*

(1.833)

Panel B: Variance equations

ω

0.000*

(2.452)

0.000*

(3.461)

0.000*

(2.628)

0.000*

(2.561)

0.000*

(2.030)

0.000

(1.588)

0.000*

(2.628)

0.000*

(2.858)

a

0.108*

(4.175)

0.192*

(4.972)

0.151*

(4.239)

0.230*

(3.799)

0.095*

(4.628)

0.090*

(3.134)

0.252*

(4.281)

0.145*

(5.235)

β

0.837*

(19.17)

0.777*

(21.01)

0.826*

(19.88)

0.670*

(7.618)

0.888*

(34.91)

0.894*

(23.78)

0.747*

(15.15)

0.820*

(24.67)

Pers. 0.945 0.969 0.977 0.900 0.983 0.984 0.999 0.965

Panel C: Multivariate DCC equation

a 0.014* (3.325)

b 0.912* (20.861)

1

2

1

2

34

Panel C. CEE - RUS

Notes: (*) denotes statistical significance at the 5%, and 1% levels, respectively. Numbers in

parentheses are Z-statistics.

Czech

Republic Estonia Hungary Poland Romania Russia Slovakia Slovenia

Panel A: Mean equations

Μ

0.003*

(2.653)

0.003*

(2.613)

0.003*

(2.473)

0.002*

(2.163)

0.004*

(2.369)

0.005*

(2.292)

0.000

(-0.015)

0.002*

(3.233)

-0.088*

(-2.608)

0.131*

(3.270)

-0.114*

(-3.498)

-0.044

(-1.289)

-0.001

(-0.001)

0.090*

(2.351)

-0.020

(-0.524)

0.123*

(2.792)

-0.003

(-0.149)

-0.004

(-0.233)

-0.023

(-0.785)

-0.003

(-0.155)

0.041

(1.490) ---

0.028*

(1.847)

-0.008

(-0.709)

Panel B: Variance equations

Ω

0.000*

(2.095)

0.000*

(3.585)

0.000

(1.487)

0.000*

(1.901)

0.000

(1.424)

0.000*

(2.777)

0.000*

(2.686)

0.000*

(3.835)

A

0.104*

(3.680)

0.190*

(4.999)

0.108*

(2.612)

0.085*

(4.238)

0.084*

(2.432)

0.149*

(5.725)

0.237*

(3.877)

0.139*

(5.477)

Β

0.850*

(18.51)

0.783*

(22.85)

0.865*

(14.72)

0.900*

(37.45)

0.901*

(20.50)

0.838*

(31.46)

0.753*

(14.71)

0.824*

(31.54)

Pers. 0.954 0.973 0.973 0.985 0.985 0.987 0.990 0.963

Panel C: Multivariate DCC equation

A 0.007* (2.272)

B 0.958* (34.441)

1

2

35

Table 6: Dynamic Conditional Correlations statistics

Average

Standard

Deviation

Trend

(*1000)

t-

statistic Δρ

Panel A: US - CEE Dynamic Conditional Correlations

Czech

Republic 0.460505 0.060639 0.18 14.40052 26.23%

Estonia 0.20528 0.054049 0.158 14.05662 58.97%

Hungary 0.468691 0.035162 0.0675 8.471359 8.91%

Poland 0.476008 0.035184 0.0883 11.57342 11.62%

Romania 0.1712 0.068145 0.226 16.80191 128.40%

Slovakia 0.071311 0.028967 0.0356 5.238905 34.70%

Slovenia 0.251996 0.058242 0.12 9.160044 32.77%

Panel B: GERMANY - CEE Dynamic Conditional Correlations

Czech

Republic 0.516741 0.048698 0.114 10.66337 14.00%

Estonia 0.298586 0.047738 0.0958 8.900177 20.99%

Hungary 0.537865 0.031413 0.0393 5.329694 4.42%

Poland 0.491964 0.036085 0.0836 10.51237 10.59%

Romania 0.18896 0.060117 0.171 13.53175 72.97%

Slovakia 0.060219 0.027692 0.014 2.118791 14.82%

Slovenia 0.28802 0.053598 0.0918 7.460731 20.83%

Panel C: RUSSIA - CEE Dynamic Conditional Correlations

Czech

Republic 0.153217 0.031135 0.00137 0.182778 0.53%

Estonia 0.233142 0.036567 0.0399 4.631142 10.68%

Hungary 0.13084 0.038236 0.0514 5.758192 26.34%

Poland 0.157858 0.028897 0.0176 2.545682 6.81%

Romania 0.225238 0.033213 0.0397 5.085543 11.01%

Slovakia 0.012306 0.029671 0.0806 12.79217 412.80%

Slovenia 0.147058 0.02338 0.0254 4.604425 10.77%

Note: “Trend” is the slope coefficient of a regression of conditional correlations ,ij t on a constant and

a time trend. t-ratio is the associated t-statistic. Δρ is the difference between the last and first fitted

values of a regression of conditional correlations on a constant and a zero-mean time trend.

36

Table 7: Dynamic correlation, volatility and the crises periods

The results are derived by estimating the regression3

, , , , ,

1

ij t j t j i t i k k t ij t

k

h h DM

, where

i = US, Germany, Russia and j = Czech Republic, Estonia, Hungary, Poland, Romania, Slovakia and

Slovenia. ,ij t is the dynamic conditional correlation between the CEE and the developed stock

markets, ht is the conditional volatility, ,k tDM are dummy variables for three crises periods: crisis in

Asia and Russia (21/11/1997-30/10/1998), the dot-com bubble (10/03/2000-27/09/2002) and the 2008

stock market crash (Lehman Brothers collapse) (26/09/2008-End of sample period).

Constant ht,j ht,i DM1,t DM2,t DM3,t R2

Panel A: US - CEE Dynamic Correlation

Czech

Republic

0.4518 26.8830 -25.6921 -0.0114 -0.0409 0.1570 0.7064

(167.5967) (10.8383) (-5.5861) (-2.2529) (-10.4878) (10.7022)

Estonia 0.1941 -0.1436 21.6331 -0.0908 -0.0002 0.0968 0.5771

(71.7178) (-0.1842) (5.2074) (-12.2794) (-0.0478) (6.6663)

Hungary 0.4546 4.8028 13.4071 -0.0295 -0.0226 0.0768 0.6948

(294.3139) (8.4810) (5.5789) (-8.8708) (-9.8775) (9.5756)

Poland 0.4680 3.3021 7.7047 0.0133 -0.0289 0.0998 0.6337

(262.5993) (2.8130) (2.8696) (3.7649) (-11.6006) (11.2708)

Romania 0.1670 3.7243 11.9017 -0.0547 -0.0679 0.1988 0.7240

(52.7495) (2.7418) (2.6289) (-9.4868) (-16.2659) (13.3178)

Slovakia 0.0707 -1.7145 -11.4994 0.0083 0.0274 0.1079 0.3034

(35.2715) (-1.5971) (-4.0790) (2.2422) (9.7919) (10.7496)

Slovenia 0.2438 22.4688 -10.9162 -0.0005 -0.0187 0.1917 0.6986

(99.3207) (6.7154) (-2.4331) (-0.0919) (-4.7225) (13.5312)

Panel B: GERMANY - CEE Dynamic Correlation

Czech

Republic

0.4944 23.5125 -3.4710 -0.0300 -0.0040 0.0664 0.5788

(208.7033) (10.6965) (-2.2669) (-6.1874) (-1.2317) (5.4230)

Estonia 0.2785 0.4135 15.8548 -0.0855 0.0131 0.0535 0.6965

(159.9406) (0.6504) (14.2081) (-14.9072) (4.8564) (6.7004)

Hungary 0.5254 1.0716 8.0738 -0.0256 -0.0042 0.0656 0.5818

(381.2156) (2.3823) (9.1679) (-7.6625) (-1.9801) (10.5771)

Poland 0.4866 -4.0275 4.4531 0.0107 -0.0025 0.1200 0.4866

(240.8992) (-2.6781) (3.8292) (2.4613) (-0.9463) (15.1881)

Romania 0.1744 6.0506 3.7108 -0.0334 -0.0351 0.1788 0.6515

(60.0254) (4.9717) (2.4481) (-5.8759) (-9.6253) (15.1256)

Slovakia 0.0561 -1.1353 3.5298 0.0019 -0.0036 0.0297 0.1210

(28.4103) (-0.9935) (3.4086) (0.4769) (-1.3630) (3.7485)

Slovenia 0.2759 30.9126 -7.9724 -0.0118 -0.0005 0.1505 0.7132

(141.9495) (11.0302) (-5.9314) (-2.6420) (-0.1582) (13.0992)

Panel C: RUSSIA - CEE Dynamic Correlation

Czech

Republic

0.1340 9.5416 2.4685 -0.0182 -0.0100 -0.0082 0.5245

(85.2129) (6.4342) (9.6144) (-4.5401) (-4.6201) (-0.9760)

Estonia 0.2351 1.9092 -4.0280 0.0759 0.0130 0.1353 0.4407

(141.8093) (2.7639) (-13.2295) (12.8536) (4.7127) (18.2365)

Hungary 0.1128 16.2212 -0.5411 -0.0875 -0.0173 -0.0019 0.6569

(77.6707) (18.7722) (-1.6532) (-21.4401) (-7.6568) (-0.3164)

Poland 0.1450 6.5345 2.3057 -0.0608 -0.0090 0.0099 0.4584

(88.9390) (4.5700) (8.5367) (-15.6929) (-4.2341) (1.7109)

Romania 0.2188 -2.4174 2.4476 0.0055 -0.0132 0.0767 0.4767

(110.6488) (-2.4091) (8.4429) (1.2514) (-5.4557) (10.4886)

Slovakia 0.0166 -1.2931 -2.1502 0.0022 0.0128 0.1185 0.4484

(10.6488) (-1.2250) (-9.6934) (0.5437) (5.7635) (19.7720)

Slovenia 0.1357 7.7716 2.2160 -0.0418 0.0000 -0.0115 0.4508

(119.1195) (4.7123) (11.8238) (-13.1928) (0.0063) (-1.6677)

37

Table 8: Dynamic correlation and Macroeconomic variables

Notes: The results are derived by estimating the regression

, 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , / ,2 2 ij t t cee t cee t cee t cee t us t us t us t cee us ij tIP IR M CR IP IR M ER ,

where IP, IR, M2, CR and ER stand for the industrial production, interest rate, money supply, credit

rating and exchange rate respectively. ,ij t is the dynamic conditional correlation between the CEE

and the developed stock markets. We applied a rolling regression methodology using a time window of

36 months. Thus we have 5544 (99 rolling periods * 8 factors * 7 CEE markets) estimated coefficients

in total. The values in the cells stand for the number of times the t-statistic of each explanatory variable

is significant at 5% level of significance. R2 is the mean adjusted coefficient of determination statistic.

CEE US Exchange

rate Mean R2

Industrial

Production

Interest

Rate

Money

Supply

Credit

Rating

Industrial

Production

Interest

Rate

Money

Supply

Czech

Republic 14 69 9 10 7 59 7 8 54,70%

Estonia 3 78 7 0 9 84 12 6 68,98%

Hungary 1 57 0 8 4 86 5 5 54,73%

Poland 7 52 3 35 9 64 23 22 47,82%

Romania 1 76 7 5 13 74 33 27 49,74%

Slovakia 3 47 5 4 34 52 28 13 43,93%

Slovenia 5 58 15 0 1 82 21 19 59,53%

38

Figure 1: Weekly Returns

-.4

-.3

-.2

-.1

.0

.1

.2

98 99 00 01 02 03 04 05 06 07 08

PX INDEX

-.3

-.2

-.1

.0

.1

.2

98 99 00 01 02 03 04 05 06 07 08

TALSE INDEX

-.3

-.2

-.1

.0

.1

.2

98 99 00 01 02 03 04 05 06 07 08

DAX INDEX

-.4

-.3

-.2

-.1

.0

.1

.2

98 99 00 01 02 03 04 05 06 07 08

BUX INDEX

-.20

-.16

-.12

-.08

-.04

.00

.04

.08

.12

98 99 00 01 02 03 04 05 06 07 08

WIG INDEX

-.4

-.3

-.2

-.1

.0

.1

.2

.3

98 99 00 01 02 03 04 05 06 07 08

BET INDEX

-.4

-.3

-.2

-.1

.0

.1

.2

.3

.4

98 99 00 01 02 03 04 05 06 07 08

RTSI INDEX

-.12

-.08

-.04

.00

.04

.08

.12

.16

.20

98 99 00 01 02 03 04 05 06 07 08

SAX INDEX

-.20

-.15

-.10

-.05

.00

.05

.10

98 99 00 01 02 03 04 05 06 07 08

SBI INDEX

-.25

-.20

-.15

-.10

-.05

.00

.05

.10

.15

98 99 00 01 02 03 04 05 06 07 08

S&P500 INDEX

39

Figure 2: Dynamic Conditional Correlations CEE – US

-.1

.0

.1

.2

.3

.4

.5

.6

.7

.8

98 99 00 01 02 03 04 05 06 07 08

BET_S&P500

BUX_S&P500

PX_S&P500

SAX_S&P500

SBI_S&P500

TALSE_S&P500

WIG_S&P500

40

Figure 3: Dynamic Conditional Correlations CEE – GERMANY

-.1

.0

.1

.2

.3

.4

.5

.6

.7

.8

98 99 00 01 02 03 04 05 06 07 08

BET_DAX

BUX_DAX

PX_DAX

SAX_DAX

SBI_DAX

TALSE_DAX

WIG_DAX

41

Figure 4: Dynamic Conditional Correlations CEE – RUSSIA

-.2

-.1

.0

.1

.2

.3

.4

98 99 00 01 02 03 04 05 06 07 08

BET_RTSI

BUX_RTSI

PX_RTSI

SAX_RTSI

SBI_RTSI

TALSE_RTSI

WIG_RTSI